Spatial variation,ecological risk and environmental pollution assessment of heavy metal of farmland soil in Mingxi County of Fujian Province
Bibliographic record
Abstract
The spatial variation of heavy metal in farmland soils was determined,and their potential ecological risk and environmental pollution were assessed. 88 top soil samples were collected from Mingxi County of Fujian Province for analyzing the concentrations of three heavy metals( Cd,Cu and Pb) in the soils,the methods of Hakanson potential ecological risk index were used in the assessment. The results showed that the average concentrations of Cd,Cu and Pb were 0. 0119,13. 9606 and 6. 8480 mg·kg-1,respectively. According to the level II of China Soil Environment Quality Standard,all the concentrations of Cd,Cu and Pb met the requirement of environment quality standard. Three heavy metal variation coefficient were from 22% to 386%,and the variation coefficients were in the order of Cd Pb Cu. The spatial relevance of Cd and Cu was quite significant,while the spatial relevance of Pb was weak. The content of Cd was high in northern Hanxian Town and northeastern Chengguan Town; the high content of Cu located in Fengxi Town,Xiafang Town,Gaiyang Town and Hanxian Town. The content of Pb was high located in northern Xuefeng Town,Chengguan Town,and eastern Shaxi Town. About 98. 86% of the total samples reached low level of the potential ecological risk of heavy metals and the overall potential ecological risk remained low( RI = 11. 03). Risk probability map demonstrated that the area around northern Hanxian Town was in highly risky.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".